import plotly.express as pximport plotly.graph_objects as go# Group data (same as before)df_grouped = eda.groupby(['DATA_ANALYST_JOB', 'NAICS2_NAME']).size().reset_index(name='Job_Count')# Shorten the industry names (NAICS2_NAME) for better readability# Assuming NAICS2_NAME has long names, we'll map them to shorter versions# Example: Replace long names with abbreviations or shorter termsindustry_short_names = {'Professional, Scientific, and Technical Services': 'Prof. Services','Administrative and Support and Waste Management and Remediation Services': 'Admin & Waste Mgmt','Health Care and Social Assistance': 'Healthcare','Finance and Insurance': 'Finance','Information': 'Info Tech','Educational Services': 'Education','Manufacturing': 'Manufacturing','Retail Trade': 'Retail','Accommodation and Food Services': 'Hospitality','Other Services (except Public Administration)': 'Other Services',# Add more mappings as needed based on your dataset}# Apply the shortened names to the dataframedf_grouped['NAICS2_NAME_SHORT'] = df_grouped['NAICS2_NAME'].map(industry_short_names).fillna(df_grouped['NAICS2_NAME'])# Define a vibrant and modern color palettecolor_map = {False: "#FF6B6B", # Coral red for FalseTrue: "#4ECDC4"# Teal for True}# Create the bar plot with Plotly Expressfig = px.bar(df_grouped, x='NAICS2_NAME_SHORT', # Use shortened names y='Job_Count', color='DATA_ANALYST_JOB', title="Data & Business Analytics Job Trends", labels={'NAICS2_NAME_SHORT': 'Industry', 'Job_Count': 'Number of Jobs'}, barmode='group', color_discrete_map=color_map )# Beautify the layout with a modern, clean designfig.update_layout(# Sizing and margins autosize=True, width=1100, # Slightly narrower for better focus height=650, # Adjusted height margin=dict(l=50, r=50, t=90, b=120), # Adjusted margins for cleaner look# Background and plot styling plot_bgcolor='rgba(240, 240, 245, 1)', # Softer gray background paper_bgcolor='rgba(255, 255, 255, 1)', # White paper background font=dict(family="Helvetica, sans-serif", size=14, color="#2D3748"), # Modern font# Title styling title=dict( text="Data & Business Analytics Job Trends", font=dict(size=26, color="#2D3748", family="Helvetica, sans-serif"), x=0.5, # Center the title xanchor="center", y=0.95, yanchor="top" ),# X-axis styling xaxis=dict( title="Industry", title_font=dict(size=18, color="#2D3748"), tickfont=dict(size=13, color="#4A5568"), tickangle=-30, # Slightly less aggressive rotation gridcolor="rgba(200, 200, 200, 0.2)", # Very light gridlines linecolor="#2D3748", linewidth=2, showline=True ),# Y-axis styling yaxis=dict( title="Number of Jobs", title_font=dict(size=18, color="#2D3748"), tickfont=dict(size=13, color="#4A5568"),range=[0, df_grouped['Job_Count'].max() *1.15], # Slightly tighter range gridcolor="rgba(200, 200, 200, 0.2)", linecolor="#2D3748", linewidth=2, showline=True ),# Legend styling legend=dict( title="Job Type", font=dict(size=13, color="#2D3748"), bgcolor="rgba(255, 255, 255, 0.95)", bordercolor="#2D3748", borderwidth=1, x=1.02, # Position outside y=0.5, xanchor="left", yanchor="middle" ),# Hover and interactivity hovermode="closest", hoverlabel=dict( bgcolor="rgba(255, 255, 255, 0.9)", font_size=13, font_family="Helvetica, sans-serif", font_color="#2D3748", bordercolor="#2D3748" ),# Add a subtle shadow effect for depth bargap=0.2, # Add space between bars for clarity)# Customize the barsfig.update_traces( marker=dict( line=dict(width=1.2, color="#2D3748"), # Thinner border for elegance# Add a gradient effect to the bars coloraxis=None, ), opacity=0.85, # Slight transparency for softness text=df_grouped['Job_Count'], # Add labels on bars textposition='outside', # Place labels outside for clarity textfont=dict(size=12, color="#2D3748", family="Helvetica, sans-serif"), texttemplate='%{text}', # Show just the number)# Add a subtle annotation for the highest job countmax_job = df_grouped.loc[df_grouped['Job_Count'].idxmax()]fig.add_annotation( x=max_job['NAICS2_NAME_SHORT'], y=max_job['Job_Count'] *1.1, text=f"Top: {max_job['NAICS2_NAME_SHORT']}<br>{max_job['Job_Count']} Jobs", showarrow=True, arrowhead=1, ax=20, ay=-40, font=dict(size=13, color="#2D3748", family="Helvetica, sans-serif"), bgcolor="rgba(255, 255, 255, 0.85)", bordercolor="#2D3748", borderwidth=1, borderpad=4)
import plotly.express as pximport plotly.graph_objects as go# Identify the top 10 industries with the most data analyst jobstop_industries = df_grouped.groupby("NAICS2_NAME")["Job_Count"].sum().nlargest(10).index# Filter the dataset for top industriesdf_top_industries = df_grouped[df_grouped["NAICS2_NAME"].isin(top_industries)].copy()# Shorten industry names for better readabilityindustry_short_names = {'Professional, Scientific, and Technical Services': 'Prof. Services','Administrative and Support and Waste Management and Remediation Services': 'Admin & Waste Mgmt','Health Care and Social Assistance': 'Healthcare','Finance and Insurance': 'Finance','Information': 'Info Tech','Educational Services': 'Education','Manufacturing': 'Manufacturing','Retail Trade': 'Retail','Accommodation and Food Services': 'Hospitality','Other Services (except Public Administration)': 'Other Services',}df_top_industries['NAICS2_NAME_SHORT'] = df_top_industries['NAICS2_NAME'].map(industry_short_names).fillna(df_top_industries['NAICS2_NAME'])# Update DATA_ANALYST_JOB labels for claritydf_top_industries['Job_Category'] = df_top_industries['DATA_ANALYST_JOB'].map({True: 'Data Analyst', False: 'Business Analyst'})# Define a modern, vibrant color palette for industries (10 colors for top 10 industries)color_palette = ['#FF6B6B', # Coral Red'#4ECDC4', # Teal'#45B7D1', # Sky Blue'#96CEB4', # Sage Green'#FFEEAD', # Soft Yellow'#D4A5A5', # Dusty Rose'#9B59B6', # Purple'#3498DB', # Bright Blue'#E67E22', # Orange'#1ABC9C', # Turquoise]color_map = {industry: color for industry, color inzip(df_top_industries['NAICS2_NAME_SHORT'].unique(), color_palette)}# Create the bar chartfig = px.bar(df_top_industries, x="Job_Category", # Use the updated labels y="Job_Count", color="NAICS2_NAME_SHORT", title="Top 10 Industries Hiring Data & Business Analysts", labels={'Job_Category': 'Job Category', 'Job_Count': 'Number of Jobs', 'NAICS2_NAME_SHORT': 'Industry'}, barmode='group')# Beautify the layoutfig.update_layout(# Sizing and margins autosize=True, width=1150, # Slightly wider for balance height=650, # Adjusted height margin=dict(l=60, r=150, t=100, b=80), # Adjusted for legend and title# Background and plot styling plot_bgcolor='rgba(245, 245, 250, 1)', # Very light lavender-gray paper_bgcolor='rgba(255, 255, 255, 1)', # White paper background font=dict(family="Poppins, sans-serif", size=14, color="#2D3436"), # Modern, elegant font# Title styling title=dict( text="Top 10 Industries Hiring Data & Business Analysts", font=dict(size=28, color="#2D3436", family="Poppins, sans-serif"), x=0.5, xanchor="center", y=0.95, yanchor="top" ),# X-axis styling xaxis=dict( title="Job Category", title_font=dict(size=18, color="#2D3436"), tickfont=dict(size=14, color="#636E72"), gridcolor="rgba(200, 200, 200, 0.1)", # Very subtle gridlines linecolor="#2D3436", linewidth=2, showline=True, tickvals=['Data Analyst', 'Business Analyst'], # Ensure clarity ),# Y-axis styling yaxis=dict( title="Number of Jobs", title_font=dict(size=18, color="#2D3436"), tickfont=dict(size=14, color="#636E72"),range=[0, df_top_industries['Job_Count'].max() *1.2], # Extended range gridcolor="rgba(200, 200, 200, 0.3)", linecolor="#2D3436", linewidth=2, showline=True ),# Legend styling legend=dict( title="Industry", font=dict(size=13, color="#2D3436"), bgcolor="rgba(255, 255, 255, 0.95)", bordercolor="#2D3436", borderwidth=1, x=1.02, # Position outside y=0.5, xanchor="left", yanchor="middle", traceorder="normal", # Ensure consistent ordering ),# Hover and interactivity hovermode="closest", hoverlabel=dict( bgcolor="rgba(255, 255, 255, 0.9)", font_size=13, font_family="Poppins, sans-serif", font_color="#2D3436", bordercolor="#2D3436", align="left" ),# Bar spacing bargap=0.25, # Add space between bars barnorm=None,)# Customize the barsfig.update_traces( marker=dict( line=dict(width=1, color="#2D3436"), # Subtle border# Add a slight gradient effect using opacity opacity=0.9, ), text=df_top_industries['Job_Count'], # Add labels on bars textposition='outside', # Place labels outside textfont=dict(size=12, color="#2D3436", family="Poppins, sans-serif"), texttemplate='%{text}', # Show just the number# Custom hover template hovertemplate="<b>%{fullData.name}</b><br>Category: %{x}<br>Jobs: %{y}<extra></extra>",)# Add a subtle gradient background effect (using shapes)fig.add_shape(type="rect", x0=0, y0=0, x1=1, y1=1, xref="paper", yref="paper", fillcolor="rgba(220, 221, 255, 0.2)", # Very light gradient overlay line=dict(width=0), layer="below")# Add an annotation for the highest job countmax_job = df_top_industries.loc[df_top_industries['Job_Count'].idxmax()]fig.add_annotation( x=max_job['Job_Category'], y=max_job['Job_Count'] *1.15, text=f"Top: {max_job['NAICS2_NAME_SHORT']}<br>{max_job['Job_Count']} Jobs", showarrow=True, arrowhead=1, ax=20, ay=-40, font=dict(size=13, color="#2D3436", family="Poppins, sans-serif"), bgcolor="rgba(255, 255, 255, 0.9)", bordercolor="#2D3436", borderwidth=1, borderpad=4)# Add a subtle shadow effect to the title (simulated with a duplicate text)fig.add_annotation( x=0.5, y=0.95, xref="paper", yref="paper", text="Top 10 Industries Hiring Data & Business Analysts", showarrow=False, font=dict(size=28, color="rgba(0, 0, 0, 0.05)", family="Poppins, sans-serif"), xanchor="center", yanchor="top", yshift=-2, xshift=2)
import plotly.express as pximport plotly.graph_objects as go# Define target industriestarget_industries = ["Finance and Insurance", "Health Care and Social Assistance"]# Filter the original DataFrame (df) instead of df_groupeddf_remote = eda[eda["NAICS2_NAME"].isin(target_industries)]# Count occurrences of each remote type in these industriesdf_remote_grouped = df_remote.groupby("REMOTE_TYPE_NAME").size().reset_index(name="Count")# Define a modern color palettecolor_map = {"Remote": "#6AB1E9", # Soft Sky Blue"On-Site": "#EF767A", # Soft Coral"Hybrid": "#49C6B7"# Teal-Green}# Create the pie chartfig = px.pie(df_remote_grouped, names="REMOTE_TYPE_NAME", values="Count", title="Remote vs. On-Site Data Analyst Jobs in Finance & Healthcare", color="REMOTE_TYPE_NAME", color_discrete_map=color_map)# Beautify the layoutfig.update_layout(# Sizing and margins autosize=True, width=900, # Compact width for pie charts height=700, # Taller height to accommodate title and legend margin=dict(l=50, r=50, t=120, b=50), # Adjusted margins for title# Background styling plot_bgcolor='rgba(240, 240, 245, 1)', # Light gray background paper_bgcolor='rgba(255, 255, 255, 1)', # White paper background font=dict(family="Poppins, sans-serif", size=14, color="#2D3436"), # Modern font# Title styling title=dict( text="Remote vs. On-Site Data Analyst Jobs in Finance & Healthcare", font=dict(size=24, color="#2D3436", family="Poppins, sans-serif"), x=0.5, xanchor="center", y=0.95, yanchor="top" ),# Legend styling legend=dict( title="Remote Type", font=dict(size=13, color="#2D3436"), bgcolor="rgba(255, 255, 255, 0.95)", bordercolor="#2D3436", borderwidth=1, x=1.05, # Position outside y=0.5, xanchor="left", yanchor="middle" ),# Hover and interactivity hovermode="closest", hoverlabel=dict( bgcolor="rgba(255, 255, 255, 0.9)", font_size=13, font_family="Poppins, sans-serif", font_color="#2D3436", bordercolor="#2D3436" ),# Add a subtle gradient background effect shapes=[dict(type="rect", x0=0, y0=0, x1=1, y1=1, xref="paper", yref="paper", fillcolor="rgba(220, 221, 255, 0.2)", # Light gradient overlay line=dict(width=0), layer="below" ) ])# Customize the pie chart slicesfig.update_traces(# Add percentages and labels textinfo="percent+label", textfont=dict(size=14, color="#2D3436", family="Poppins, sans-serif"), textposition="inside", # Place labels inside for a cleaner look# Add a subtle border to slices marker=dict( line=dict(color="#2D3436", width=1.5) ),# Pull out the largest slice slightly for emphasis pull=[0.1if count == df_remote_grouped['Count'].max() else0for count in df_remote_grouped['Count']],# Add a slight opacity for a softer look opacity=0.9,# Custom hover template hovertemplate="<b>%{label}</b><br>Count: %{value}<br>Percentage: %{percent}<extra></extra>",# Add a rotation for better presentation rotation=45,# Add a subtle shadow effect to slices (simulated with opacity and border) sort=False# Keep the order as in the data)# Add an annotation for the largest slicemax_slice = df_remote_grouped.loc[df_remote_grouped['Count'].idxmax()]fig.add_annotation( x=0.5, y=0.1, xref="paper", yref="paper", text=f"Dominant Type: {max_slice['REMOTE_TYPE_NAME']}<br>{max_slice['Count']} Jobs ({fig.data[0]['values'].tolist().index(max_slice['Count'])*100/sum(fig.data[0]['values']):.1f}%)", showarrow=False, font=dict(size=13, color="#2D3436", family="Poppins, sans-serif"), bgcolor="rgba(255, 255, 255, 0.9)", bordercolor="#2D3436", borderwidth=1, borderpad=4)# Add a shadow effect to the title (simulated with a duplicate text)fig.add_annotation( x=0.5, y=0.95, xref="paper", yref="paper", text="Remote vs. On-Site Data Analyst Jobs in Finance & Healthcare", showarrow=False, font=dict(size=24, color="rgba(0, 0, 0, 0.05)", family="Poppins, sans-serif"), xanchor="center", yanchor="top", yshift=-2, xshift=2)
import pandas as pdimport matplotlib.pyplot as plt# 1) Trim out the 1–99th percentile so outliers don’t skew the meanslow, high = eda['SALARY'].quantile([0.01, 0.99])eda_trim = eda[(eda['SALARY'] >= low) & (eda['SALARY'] <= high)]# 2) Compute mean salary by remote typemeans = eda_trim.groupby('REMOTE_TYPE_NAME')['SALARY'].mean().loc[['Remote', 'Not Remote', 'Hybrid Remote']]# 3) Plot with value labelsplt.figure(figsize=(10, 7))bars = means.plot.bar(color=['#4C72B0', '#55A868', '#C44E52'])plt.title('Average Salary by Remote Type (1–99th pct)')plt.ylabel('Mean Annual Salary')plt.xticks(rotation=0)# Add value labels on top of each barfor bar in bars.patches: plt.text(bar.get_x() + bar.get_width() /2, bar.get_height(), f'{int(bar.get_height())}', ha='center', va='bottom')
import pandas as pdimport matplotlib.pyplot as plt# Trim outlierslow, high = eda['SALARY'].quantile([0.01, 0.99])eda_trim = eda[(eda['SALARY'] >= low) & (eda['SALARY'] <= high)]# Plot histogramplt.figure(figsize=(8, 6))plt.hist(eda_trim['SALARY'], bins=30, color='#4C72B0', edgecolor='black')plt.title('Salary Distribution (1–99th pct)')plt.xlabel('Annual Salary')plt.ylabel('Frequency')
Text(0, 0.5, 'Frequency')
import plotly.express as pximport pandas as pd# Define target industries (same as before)target_industries = ["Finance and Insurance", "Health Care and Social Assistance"]# Filter the original DataFrame for these industries# Assuming 'df' has a 'SALARY' column (from our regression analysis context)df_target = eda[eda["NAICS2_NAME"].isin(target_industries)].copy()# Map DATA_ANALYST_JOB to meaningful labelsdf_target['Job_Category'] = df_target['DATA_ANALYST_JOB'].map({True: 'Data Analyst', False: 'Business Analyst'})# Group by Job_Category and REMOTE_TYPE_NAME, calculate average salarydf_salary_grouped = df_target.groupby(['Job_Category', 'REMOTE_TYPE_NAME'])['SALARY'].mean().reset_index(name='Avg_Salary')# Create a grouped bar chartfig = px.bar(df_salary_grouped, x='Job_Category', y='Avg_Salary', color='REMOTE_TYPE_NAME', title="Average Salary by Remote Type and Job Category (Finance & Healthcare)", labels={'Job_Category': 'Job Category', 'Avg_Salary': 'Average Salary ($)', 'REMOTE_TYPE_NAME': 'Remote Type'}, barmode='group', color_discrete_map={"Remote": "#6AB1E9", "On-Site": "#EF767A", "Hybrid": "#49C6B7"})# Beautify the layoutfig.update_layout( width=900, height=600, plot_bgcolor='rgba(240, 240, 245, 1)', paper_bgcolor='rgba(255, 255, 255, 1)', font=dict(family="Poppins, sans-serif", size=14, color="#2D3436"), title=dict( text="Average Salary by Remote Type and Job Category (Finance & Healthcare)", font=dict(size=24, color="#2D3436"), x=0.5, xanchor="center" ), xaxis=dict( title="Job Category", title_font=dict(size=18), tickfont=dict(size=14) ), yaxis=dict( title="Average Salary ($)", title_font=dict(size=18), tickfont=dict(size=14), gridcolor="rgba(200, 200, 200, 0.3)" ), legend=dict( title="Remote Type", font=dict(size=13), x=1.05, y=0.5, xanchor="left", yanchor="middle" ), bargap=0.2)# Add value labels on top of barsfig.update_traces( text=df_salary_grouped['Avg_Salary'].round(2), textposition='outside', textfont=dict(size=12, color="#2D3436"))
